No model-specific details available - Keras/TensorFlow

Hello AlexT,

Thanks for your asnwer.

As I mentioned at the begging of my post, I used Dataiku interface for deep learning.

Which means I build a model using TensorFlow functional API.

Dataiku documentation doesn't tell you to implement predict_proba() method when you're using that, as you do when you create a class deriving from sklearn BaseEstimator.

I don't think that's the case with TensorFlow, right?

I did a test, I used default code that Dataiku puts in the "Architecture" tab of deep learning GUI, which is:

def build_model(input_shapes, n_classes=None):

    # This input will receive all the preprocessed features
    # sent to 'main'
    input_main = Input(shape=input_shapes["main"], name="main")

    x = Dense(64, activation='relu')(input_main)
    x = Dense(64, activation='relu')(x)

    predictions = Dense(n_classes, activation='softmax')(x)

    # The 'inputs' parameter of your model must contain the
    # full list of inputs used in the architecture
    model = Model(inputs=[input_main], outputs=predictions)

    return model

As you can see, final layer uses softmax, which returns probabilities per class.

Still, the only thing I can see in the GUI is "No model-specific details available".

I also checked the logs and there is no "Cannot draw background rows and compute column importance: model is not probabilistic" there.

Thanks AlexT, I've opened a support ticket: 58374